Download Why can`t a computer translate more like a person?

[Written 1995; copied (2005) from: http://www.fortunecity.com/business/reception/19/akmelby.htm]
Why Can't a Computer Translate More Like a Person?
by Alan K. Melby
Posters and Telescopes: an Introduction to Translation
Translation is difficult, even for people. To begin with, you have to know two languages
intimately. And even if you speak two or more languages fluently, it is not a trivial matter to
produce a good translation. When people start talking about the possibility of a computer
replacing a human translator, someone will often bring up a sentence similar to the following:
* Time flies like an arrow.
The person who brought it up will typically conclude by asserting that this sentence is an
obvious example of a sentence that a computer could not translate. As a matter of fact, a
computer could handle this sentence if it were programmed to handle just this sentence. The
problem is getting a computer to deal adequately with sentences it has not been specifically
programmed to handle. I will partially analyze this sentence and then give other superficially
similar sentences that cannot all be translated in a parallel fashion.
This sample sentence about time flying is a figure of speech that combines a metaphor and a
simile. Time does not really fly in the literal sense of a bird flying, but here we metaphorically
say it does. Another example of a metaphor would be when we say that a ship ploughs the
water, since there is no real plough and no dirt involved. The simile in this expression is the
comparison between the metaphorical flight of time with the flight path of an arrow.
Now consider the following sentence, which is a rather dumb-sounding figure of speech
modeled on the first one:
* Fruit flies like a baseball.
Not all fruit, when thrown, would fly through the air like a baseball, except perhaps an apple,
orange, or peach. But wait a minute. Suppose you substitute ‘peach’ for ‘baseball’ in the second
sentence. All of a sudden, there is a new meaning. This time everything is literal. The ‘fruit
flies’ are pesky little insects you can see crawling around on a juicy peach, having a feast. The
‘peach’ version of the sentence would be translated very differently from the ‘arrow’ version or
the ‘baseball’ version.
The point of these sentences for human versus computer translation is that a human translator
would know to handle the variation “Fruit flies like a peach” very differently from the baseball
version while a computer would probably not even notice the difference and therefore could
never replace a human translator. Why wouldn’t a computer notice the difference? We will
explore differences between humans and computer throughout this paper.
These sentences do show how words can shift in their usage. The word ‘flies’ shifts from
signifying an action to signifying an insect, and in most languages it cannot be translated the
same way in both usages. But we do not need anything nearly so exotic as these sentences in
order to show that translation is full of pitfalls. Let me give you an example of a human
translation of a simple poster, a translation that did not turn out very well.
This summer I attended a conference in Luxembourg and noticed in the train station a poster
announcing a coming event. The announcement was in French with an English translation. I will
refer to this announcement as the poster example. The English translation of the date and time of
the event read as follows:
* Saturday the 24 June 1995 to 17 o'clock
Obviously, there are a number of problems in this translation. In English we say “the 24th of
June, 1995” or “June 24, 1995,” rather than “the 24 June 1995.” Also, we say “5 o'clock” or “5
p.m.,” because in the United States we divide the day into two 12-hour periods, rather than one
24-hour period, except in the military. In England, the use of a 24-hour clock is more common
but even there one would not say “17 o'clock.” Perhaps the most puzzling error in this
translation is the use of the word ‘to’. At first glance, one would assume that the word ‘at’ was
intended, so that the translation becomes, after all our changes:
* Saturday, the 24th of June at 5 p.m.
However, an examination of the French shows that this is incorrect. The French original used
the word vers, which can mean either ‘in the direction of’ (as in a movement toward an object or
to the left) or ‘at an approximate time’ (as in a promise to drop off a package around noon).
Clearly, the second reading of vers is more likely here. Whoever translated the French probably
used a French to English dictionary and just picked the first translation listed under the word
vers, without thinking about whether it would work in this context. In the case of this poster, the
translator did not have a sufficient knowledge of both languages, and the translation turned out
not only awkward but just plain wrong.
This example of bad human translation is interesting because it was most likely done by a
human yet in a manner similar to the way computers translate. (By the way, the conference I
was attending in Luxembourg, where I saw the poster, was the fifth world summit on computer
translation, which is usually called Machine Translation, hence the conference title: Machine
Translation Summit V.)
Computers do not really think about what they are doing. They just mechanically pick a
translation for each word of the source text, that is, the text being translated, without
understanding what they are translating and without considering the context. An examination of
the source text for our poster example will illustrate this.
* French source text: le samedi 24 juin 1995 vers 17h00
* Poster translation: Saturday the 24 June 1995 to 17 o'clock
* Better translation: Saturday, the 24th of June, 1995, around 5 p.m.
To give credit where it is due, the translator apparently knew enough about English dates to
reposition the translation of the French article le to the other side of ‘Saturday.’ Other than the
re-ordering of the article, the translation on the poster could be obtained using a simple wordfor-word substitution technique by either a person or a computer looking up words in a
dictionary. No real knowledge of either language would be required. Thus, people can easily
translate like computers, that is, mechanically, usually with rather disappointing results.
However, the opposite is not true. Computers cannot, in general, translate like people, at least
not like people who know both languages and are skilled translators. I have analyzed a poor
quality human translation and provided an improved human translation. We will now look at a
real-life example of machine translation. I will refer to it as the telescope example.
Last year I was at another conference on machine translation, this one being held at Cranfield
University in the England. There were several major companies in the exhibit area
demonstrating their commercial machine translation systems. On the way, I had picked up a
French magazine similar to the American magazine Air and Space, and at the conference I fed a
sentence from the magazine into one of the machine translation systems. Below is the French
sentence that went in, followed by the English translation that came out of the computer.
* French source sentence: L'atmosphère de la Terre rend un peu myopes mêmes les meilleurs de
leur téléscopes.
* English machine translation: The atmosphere of the Earth returns a little myopes same the best
ones of their telescopes.
Even without knowing French, one can see that the English translation is basically the result of a
word-for-word substitution. In the poster example, the translation was awkward and somewhat
misleading. This translation is perhaps even worse: it is practically incomprehensible. The
context of the source text is an article from a French magazine discussing the problem of
turbulence in the atmosphere. The magazine is addressed to a general audience rather than to
professional astronomers. One possible human translation would be the following:
* The earth's atmosphere makes even the best of their telescopes a little "near sighted" (in the
sense that distant objects are slightly blurred).
There are obviously a number of problems in the machine translation. These problems stem
from the ambiguity of word meanings. For example, the French verb rend can be translated as
‘return’ or ‘make,’ depending on the context. The French word même can be translated as
‘same’ or ‘even,’ again depending on the context. In both cases, the computer mechanically
chose a translation and in both cases the poor thing got it dead wrong. It is hard to tell whether
the computer couldn’t find the word myopes in its dictionary and just passed it through
unchanged or whether it found it and translated it inappropriately for the audience. A ‘myope’ is
a technical term in English for someone who is myopic, that is, near- sighted. However, this is
the wrong level of language to use in a publication intended for a general audience. Computers
have no sense of audience; they just blindly follow rules. Another machine translation system,
when given the same French sentence, did better in some ways but made other mistakes:
* The atmosphere of the Earth renders a same myopic bit best of their telescopes.
Professional human translators seldom, if ever, make errors like the ones we have seen in the
poster example and the telescope example. Nevertheless, humans with nothing but a dictionary
in hand can choose to stoop to the level of computers. In contrast, computers have not risen to
the level of professional human translators. Why not? Why can't a computer translate more like
a person?
It is interesting to observe how various persons who have not worked on machine translation
react to the title question of this paper. Some believe that there is no fundamental difference
between humans and machines. They assume that the quality of machine translation will
someday rival the quality of human translation in all respects. They point out that computers can
do arithmetic much faster and more accurately than people. Then they remind us that math is
harder than language for many students. Furthermore, they take it as obvious that the human
brain is ultimately a type of computer. From this basis, they conclude that it is just a matter of
time until we have a new kind of computer that will function like the brain, only faster and
better, and will surpass the capabilities of humans in the area of language processing. Others
take a contrary position. They believe that humans and computers are so entirely different in the
way they work inside that computers will never approach the capabilities of human translators.
Still others are puzzled by the question. They were under the impression that the problem of
machine translation was solved years ago.
The fact of the matter is that machine translation is a problem that is far from solved. Experts in
the field agree that computers do not yet translate like people. On some texts, particularly highly
technical texts treating a very narrow topic in a rather dry and monotonous style, computers
sometimes do quite well. (In the annex to this paper, I give a sentence of English and its
computer- generated translation that was offered by a vendor as part of a showcase example of
machine translation.) But with other texts, particularly with texts that are more general and more
interesting to humans, computers are very likely to produce atrocious results. Professional
human translators, on the other hand, can produce good translations of many kinds of text.
People can handle a range of text types; computers cannot. Where the experts disagree is on the
question of why computers are so limited in their ability to translate. I will present an answer to
this controversial question, but only at the end of this paper. I will build up to it in the following
stages:
•
First, I will present a few more examples of why translation is difficult for both people
and computers, even for people who know two languages and for computers that are
carefully programmed to translate.
•
Secondly, I will very briefly describe the mainstream approach to characterizing human
language and point out how it fails to address the difficulties presented in the first stage.
•
Thirdly, I will discuss a key factor that is missing in current theories of human language,
a factor that I believe will be needed in computers for them to be able to translate more
like people.
Some Difficulties in Translation
One difficulty in translation stems from the fact that most words have multiple meanings.
Because of this fact, a translation based on a one-to-one substitution of words is seldom
acceptable. We have already seen this in the poster example and the telescope example. Whether
a translation is done by a human or a computer, meaning cannot be ignored. I will give some
more examples as evidence of the need to distinguish between possible meanings of a word
when translating.
A colleague from Holland recounted the following true experience. He was traveling in France
and decided to get a haircut. He was a native speaker of Dutch and knew some French; however,
he was stuck when it came to telling the female hairdresser that he wanted a part in his hair. He
knew the Dutch word for a part in your hair and he knew one way that Dutch word could be
translated into French. He wasn’t sure whether that translation would work in this situation, but
he tried it anyway. He concluded that the French word did not convey both meanings of the
Dutch word when the hairdresser replied, “But, Monsieur, we are not even married!” It seems
that the Dutch expression for a separation of your hair (a part) and a permanent separation of a
couple (a divorce) are the same word. When you think about it, there is a logical connection, but
we are not conscious of it in English even though you can speak of a parting of your hair or a
parting of ways between two people. In French, there is a strong separation of the two concepts.
To translate the Dutch word for ‘part’ or ‘divorce’ a distinction must be made between these two
meanings. We will refer to this incident as the haircut example. Some questions it raises are
these: How does a human know when another use of the same word will be translated as a
different word? And how would a computer deal with the same problem?
We expect a word with sharply differing meanings to have several different translations,
depending on how the word is being used (Oxford English Dictionary, 1989, pp. 930-931). The
financial sense evolved from the money changer’s table or shelf, which was originally placed on
a mound of dirt. Later the same word came to represent the institution that takes care of money
for people. The river meaning has remained more closely tied to the original meaning of the
word. Even though there is an historical connection between the two meanings of ‘bank,’ we do
not expect their translation into another language to be the same, and it usually will not be the
same. This example further demonstrates the need to take account of meaning in translation. A
human will easily distinguish between the two uses of ‘bank’ and simply needs to learn how
each meaning is translated. How would a computer make the distinction?
Another word which has evolved considerably over the years is the British word ‘treacle,’ which
now means ‘molasses.’ It is derived from a word in Ancient Greek that referred to a wild
animal. One might ask how in the world it has come to mean molasses. A colleague, Ian Kelly,
supplied me with the following history of ‘treacle’. The original word for a wild animal came to
refer to the bite of a wild animal. Then the meaning broadened out to refer to any injury. It later
shifted to refer to the medicine used to treat an injury. Still later, it shifted to refer to a sweet
substance mixed with a medicine to make it more palatable. And finally, it narrowed down to
one such substance, molasses. At each step along the way, the next shift in meaning was
unpredictable, yet in hindsight each shift was motivated by the previous meaning. This
illustrates a general principle of language. At any point in time, the next shift in meaning for a
word is not entirely unlimited. We can be sure it will not shift in a way that is totally
unconnected with its current meaning. But we cannot predict exactly which connection there
will be between the current meaning and the next meaning. We cannot even make a list of all the
possible connections. We only know there will be a logical connection, at least as analyzed in
hindsight.
What are some implications of the “haircut”, “bank”, and “treacle” examples? To see their
importance to translation, we must note that words do not develop along the same paths in all
languages. Simply because there is a word in Dutch that means both ‘part’ and ‘divorce’ does
not mean that there will be one word in French with both meanings. We do not expect the two
meanings of ‘bank’ to have the same translation in another language. We do not assume that
there is a word in Modern Greek that means ‘molasses’ and is derived from the Ancient Greek
word for ‘wild animal’ just because there is such a word in British English. Each language
follows its own path in the development of meanings of words. As a result, we end up with a
mismatch between languages, and a word in one language can be translated several different
ways, depending on the situation. With the extreme examples given so far, a human will easily
sense that multiple translations are probably involved, even if a computer would have difficulty.
What causes trouble in translation for humans is that even subtle differences in meaning may
result in different translations. I will give a few examples.
The English word ‘fish’ can be used to refer to either a live fish swimming in a river (the one
that got away), or a dead fish that has been cleaned and is ready for the frying pan. In a sense,
English makes a similar distinction between fish and seafood, but ‘fish’ can be used in both
cases. Spanish makes the distinction obligatory. For the swimming fish, one would use pez and
for the fish ready for the frying pan one would use pescado. It is not clear how a speaker of
English is supposed to know to look for two translations of ‘fish’ into Spanish. The result is that
an unknowledgable human may use the wrong translation until corrected.
The English expression ‘thank you’ is problematical going into Japanese. There are several
translations that are not interchangeable and depend on factors such as whether the person being
thanked was obligated to perform the service and how much effort was involved. In English, we
make various distinctions, such as ‘thanks a million’ and ‘what a friend,’ but these distinctions
are not stylized as in Japanese nor do they necessarily have the same boundaries. A human can
learn these distinctions through substantial effort. It is not clear how to tell a computer how to
make them.
Languages are certainly influenced by the culture they are part of. The variety of thanking words
in Japanese is a reflection of the stylized intricacy of the politeness in their culture as observed
by Westerners. The French make an unexpected distinction in the translation of the English
word ‘nudist.’ Some time ago, I had a discussion with a colleague over its translation into
French. We were reviewing a bilingual French and English dictionary for its coverage of
American English versus British English, and this word was one of many that spawned
discussion. My colleague, who had lived in France a number of years ago, thought the French
word nudiste would be the best translation. I had also lived in France on several occasions,
somewhat more recently than him, and had only heard the French word naturiste used to refer to
nude beaches and such. Recently, I saw an article in a French news magazine that resolved the
issue. The article described the conflict between the nudistes and the naturistes in France. There
was even a section in the article that explained how to tell them apart. A nudiste places a high
value on a good suntan, good wine, and high-fashion clothes when away from the nudist camp.
A naturiste neither smokes nor drinks and often does yoga or transcendental meditation, prefers
homeopathic medicine, supports environmental groups, wears simple rather than name-brand
clothing when in public, and tends to look down on a nudiste. There is currently a fight in
France over which nude beaches are designated naturiste and which are designated nudiste.
Leave it to the French, bless their souls, to elevate immodesty to a nearly religious status. I trust
my French colleagues will not take offense.
The verb ‘to run’ is a another example of a word that causes a lot of trouble for translation. In a
given language, the translation of ‘run’ as the next step up in speed from jogging will not
necessarily be the same word as that used to translate the expression ‘run a company’ or ‘run
long’ (when referring to a play or meeting) or ‘run dry’ (when referring to a river). A computer
or an inexperienced human translator will often be insensitive to subtle differences in meaning
that affect translation and will use a word inappropriately. Significantly, there is no set list of
possible ways to use ‘run’ or other words of general vocabulary. Once you think you have a
complete list, a new use will come up. In order to translate well, you must first be able to
recognize a new use (a pretty tricky task for a computer) and then be able to come up with an
acceptable translation that is not on the list.
The point of this discussion of various ways to translate ‘fish,’ ‘thank you,’ ‘nudist,’ and ‘run’ is
that it is not enough to have a passing acquaintance with another language in order to produce
good translations. You must have a thorough knowledge of both languages and an ability to deal
with differences in meaning that appear insignificant until you cross over to the other language.1
Indeed, you must be a native or near-native speaker of the language you are translating into and
very strong in the language you are translating from. Being a native or near-native speaker
involves more than just memorizing lots of facts about words. It includes having an
understanding of the culture that is mixed with the language. It also includes an ability to deal
with new situations appropriately. No dictionary can contain all the solutions since the problem
is always changing as people use words in usual ways. These usual uses of words happen all the
time. Some only last for the life of a conversation or an editorial. Others catch on and become
part of the language. Some native speakers develop a tremendous skill in dealing with the
subtleties of translation. However, no computer is a native speaker of a human language. All
computers start out with their own language and are ‘taught’ human language later on. They
never truly know it the way a human native speaker knows a language with its many levels and
intricacies. Does this mean that if we taught a computer a human language starting the instant it
came off the assembly line, it could learn it perfectly? I don't think so. Computers do not learn in
the same way we do. We could say that computers can't translate like humans because they do
not learn like humans. Then we still have to explain why computers don't learn like humans.
What is missing in a computer that is present in a human? Building on the examples given so
far, I will describe three types of difficulty in translation that are intended to provide some
further insight into what capabilities a computer would need in order to deal with human
language the way humans do, but first I will make a distinction between two kinds of language.
Certainly, in order to produce an acceptable translation, you must find acceptable words in the
other language. Here we will make a very important distinction between two kinds of language:
general language and specialized terminology. In general language, it is undesirable to repeat the
same word over and over unnecessarily. Variety is highly valued. However, in specialized
terminology, consistency (which would be called monotony in the case of general language) is
highly valued. Indeed, it is essential to repeat the same term over and over whenever it refers to
the same object. It is frustrating and potentially dangerous to switch terms for the same object
when describing how to maintain or repair a complex machine such as a commercial airplane.
Now, returning to the question of acceptable translation, I said that to produce an acceptable
translation, you must find acceptable words. In the case of specialized terminology, it should be
the one and only term in the other language that has been designated as the term in a particular
language for a particular object throughout a particular document or set of documents. In the
case of general vocabulary, there may be many potential translations for a given word, and often
more than one (but not all) of the potential translations will be acceptable on a given occasion in
a given source text. What determines whether a given translation is one of the acceptable ones?
Now I return to the promised types of translation difficulty. The first type of translation
difficulty is the most easily resolved. It is the case where a word can be either a word of general
vocabulary or a specialized term. Consider the word ‘bus.’ When this word is used as an item of
general vocabulary, it is understood by all native speakers of English to refer to a roadway
vehicle for transporting groups of people. However, it can also be used as an item of specialized
terminology. Specialized terminology is divided into areas of knowledge called domains. In the
domain of computers, the term ‘bus’ refers to a component of a computer that has several slots
into which cards can be placed . One card may control a CD-ROM drive. Another may contain a
fax/modem. If you turn off the power to your desktop computer and open it up, you can
probably see the ‘bus’ for yourself.
As always, there is a connection between the new meaning and the old. The new meaning
involves carrying cards while the old one involves carrying people. In this case, the new
meaning has not superseded the old one. They both continue to be used, but it would be
dangerous, as we have already shown with several examples, to assume that both meanings will
be translated the same way in another language. The way to overcome this difficulty, either for a
human or for a computer, is to recognize whether we are using the word as an item of general
vocabulary or as a specialized term.
Humans have an amazing ability to distinguish between general and specialized uses of a word.
Once it has been detected that a word is being used as a specialized term in a particular domain,
then it is often merely a matter of consulting a terminology database for that domain to find the
standard translation of that term in that domain. Actually, it is not always as easy as I have
described it. In fact, it is common for a translator to spend a third of the time needed to produce
a translation on the task of finding translations for terms that do not yet appear in the
terminology database being used. Where computers shine is in retrieving information about
terms. They have a much better memory than humans. But computers are very bad at deciding
which is the best translation to store in the database. This failing of computers confirms our
claim that they are not native speakers of any human language in that they are unable to deal
appropriately with new situations.
When the source text is restricted to one particular domain, such as computers, it has been quite
effective to program a machine translation system to consult first a terminology database
corresponding to the domain of the source text and only consult a general dictionary for words
that are not used in that domain. Of course, this approach does have pitfalls. Suppose a text
describes a very sophisticated public transportation vehicle that includes as standard equipment
a computer. A text that describes the use of this computer may contain the word ‘bus’ used
sometimes as general vocabulary and sometimes as a specialized term. A human translator
would normally have no trouble keeping the two uses of ‘bus’ straight, but a typical machine
translation system would be hopelessly confused. Recently, this type of difficulty was illustrated
by an actual machine translation of a letter. The letter began “Dear Bill” and the machine, which
was tuned into the domain of business terms, came up with the German translation Liebe
Rechnung, which means something like “Beloved Invoice.”
This first type of difficulty is the task of distinguishing between a use of a word as a specialized
term and its use as a word of general vocabulary. One might think that if that distinction can be
made, we are home free and the computer can produce an acceptable translation. Not so. The
second type of difficulty is distinguishing between various uses of a word of general vocabulary.
We have already seen with several examples (‘fish’, ‘run,’ etc.) that it is essential to distinguish
between various general uses of a word in order to choose an appropriate translation. What we
have not discussed is how that distinction is made by a human and how it could be made by a
computer.
Already in 1960, an early machine translation researcher named Bar-Hillel provided a now
classic example of the difficulty of machine translation. He gave the seemingly simple sentence
“The box is in the pen.” He pointed out that to decide whether the sentence is talking about a
writing instrument pen or a child’s play pen, it would be necessary for a computer to know
about the relative sizes of objects in the real world . Of course, this two-way choice, as difficult
as it is for a human, is a simplification of the problem, since 'pen' can have other meanings, such
as a short form for ‘penitentiary’ or another name for a female swan. But restricting ourselves to
just the writing instrument and play pen meanings, only an unusual size of box or writing
instrument would allow an interpretation of ‘pen’ as other than an enclosure where a child plays.
The related sentence, “the pen is in the box,” is more ambiguous . Here one would assume that
the pen is a writing instrument, unless the context is about unpacking a new play pen or packing
up all the furniture in a room. The point is that accurate translation requires an understanding of
the text, which includes an understanding of the situation and an enormous variety of facts about
the world in which we live. For example, even if one can determine that, in a given situation,
‘pen’ is used as a writing instrument, the translation into Spanish varies depending on the
Spanish-speaking country.
The third type of difficulty is the need to be sensitive to total context, including the intended
audience of the translation. Meaning is not some abstract object that is independent of people
and culture. We have already seen in examples such as the translation of ‘thank you’ in Japanese
a connection between culture and distinctions made in vocabulary. Several years ago, I
translated a book on grammar from French to English. It was unfortunately not well received by
English-speaking linguists. There were several reasons, but one factor was the general rhetorical
style used by French-speaking linguists: they consider it an insult to the reader to reveal the
main point of their argument too soon. From the point of view of an English- speaking linguist,
the French linguist has forgotten to begin with a thesis statement and then back it up. Being
sensitive to the audience also means using a level of language that is appropriate. Sometimes a
misreading of the audience merely results in innocuous boredom. However, it can also have
serious long- term effects.
A serious example of insensitivity to the total context and the audience is the translation of a
remark made by Nikita Khrushchev in Moscow on November 19, 1956. Khrushchev was then
the head of the Soviet Union and had just given a speech on the Suez Canal crisis. Nassar of
Egypt threatened to deny passage through the canal. The United States and France moved to
occupy the canal. Khrushchev complained loudly about the West. Then, after the speech,
Khrushchev made an off-hand remark to a diplomat in the back room. That remark was
translated “We will bury you” and was burned into the minds of my generation as a warning that
the Russians would invade the United States and kill us all if they thought they had a chance of
winning. Several months ago, I became curious to find out what Russian words were spoken by
Khrushchev and whether they were translated appropriately. Actually, at the time I began my
research I had the impression that the statement was made by Khrushchev at the United Nations
at the same time he took off his shoe and pounded it on the table. After considerable effort by
several people, most notably my daughter Yvette, along with the help of Grant Harris of the
Library of Congress, Professor Sebastian Shaumyan, a Russian linguist, Professor Bill Sullivan
of the University of Florida, Professor Don Jarvis of Brigham Young University, and Professor
Sophia Lubensky of the State University of New York at Albany, I have been able to piece
together more about what was actually said and intended.
The remark was not ever reported by the official Russian Press. Rather it was reported by a
Russian- language newspaper called Novoe Russkoe Slovo, run by Russian emigres in the
United States. It reported that along with the famous remark, Khrushchev said flippantly that “If
we believed in God, He would be on our side.” In Soviet Communist rhetoric, it is common to
claim that history is on the side of Communism, referring back to Marx who argued that
Communism was historically inevitable. Khrushchev then added that Communism does not
need to go to war to destroy Capitalism. Continuing with the thought that Communism is a
superior system and that Capitalism will self-destruct, he said, rather than what was reported by
the press, something along the lines of “Whether you like it or not, we will be present at your
burial,” clearly meaning that he was predicting that Communism would outlast Capitalism.
Although the words used by Khrushchev could be literally translated as “We will bury you,”
(and, unfortunately, were translated that way) we have already seen that the context must be
taken into consideration. The English translator who did not take into account the context of the
remark, but instead assumed that the Russian word for “bury” could only be translated one way,
unnecessarily raised tensions between the United States and the Soviet Union and perhaps
needlessly prolonged the Cold War.
We have identified three types of translation difficulty: (1) distinguishing between general
vocabulary and specialized terms, (2) distinguishing between various meanings of a word of
general vocabulary, and (3) taking into account the total context, including the intended
audience and important details such as regionalisms. We will now look at mainstream linguistic
theory to see how well it addresses these three types of difficulty. If mainstream linguistic
theory does not address them adequately, then machine translation developers must look
elsewhere for help in programming computers to translate more like humans.
Does Mainstream Linguistic Theory Come to the Rescue?
Mainstream linguistic theory emphasizes grammatical relations in a sentence. It is essentially a
sophisticated form of sentence diagramming. Depending on when and where you went to high
school, you may have encountered sentence diagramming or you may have missed it entirely. A
sentence diagram shows all the words of a sentence and how they fit together. Mainstream
linguistic theory has added a new dimension to sentence diagramming: Universal Grammar.
According to Universal Grammar, there is only one method of diagramming sentences, this
method applies to all the languages of the world, and it is universal because it is genetically
encoded into the brain of every human child. This is a bold thesis and the large number of
linguists around the world are working within this approach. Unfortunately, whether Universal
Grammar is indeed universal or not, it says very little about the meaning of an individual word.
It classifies words only according to the grammatical categories of nouns, verbs, adjectives,
adverbs, and prepositions.
Not surprisingly, given the way it ignores word meanings, mainstream linguistics does not stack
up very well when presented with the three types of translation difficulty we have discussed. It
makes no mention of the distinction between general vocabulary and specialized terminology.
This is because mainstream linguistics does not really deal with language in its entirety. It deals
only with relatively uninteresting sentences that can be analyzed in isolation. Essentially, it deals
with one very narrow slice of the pie of language that only appears to include general
vocabulary and then calls that piece the whole pie. If it is true that mainstream linguistics does
not really deal with the general vocabulary in all its richness, then it should be no shock to learn
that it ignores the basic fact we have been exploring, namely, that a word can have several
meanings, even within the same grammatical category. And if mainstream linguistics ignores
the meanings of words, it has no need to take into account the context of a sentence. In fact, it
has been a firm principle of mainstream linguistics for many years that the proper object of
study is a single sentence in isolation, stripped of its context, its purpose, and its audience. This
treatment of language on a local level (sentence by sentence) rather than on a global level has
influenced the design of machine translation systems and we have seen the results in the
telescope example.
It is a big job to take on the mainstream approach in any field. Actually, I am not saying that the
mainstream is totally wrong. It does have many interesting things to say about grammar. Instead,
I am saying that grammar, no matter how interesting it may be, is far from sufficient to teach a
computer how to translate more like a person. Although none of the past three Barker lectures
has dealt directly with translation, I detect in them considerable support for my thesis about the
insufficiency of mainstream linguistics to deal with meaning, which I have shown to be highly
relevant to translation. I trust my three colleagues would agree that mainstream linguistics does
not treat meaning adequately.
Taking the past three Barker lectures in the order they were presented, we will begin with John
Robertson, who warned us against the dangers of unwarranted reductionism. Robertson uses
reductionism to describe an unwarranted oversimplification of a problem that leaves out an
essential element. Reductionism, in a broader sense that I will use in this paper, is an approach
commonly used in science. Reductionism, as suggested by the name, reduces a complex
phenomenon to simple underlying components. It has in some areas been spectacularly
successful, such as the reduction of visible light, infrared heat, radio waves, and x-rays to
variations of a single phenomenon called electromagnetic radiation. But as Robertson points out,
reductionism can go too far. In linguistics, the reduction of language to grammar separated from
meaning is a highly unwarranted instance of reductionism. It may give the appearance of
allowing a scientific study of grammar, but ultimately it is a dead end approach that will not
form a solid basis for studying other aspects of language beyond grammar and will not even
allow a fully satisfying explanation of grammar.
My second colleague and Barker lecturer, Cheryl Brown, argued for the importance of words
over grammar. Mainstream linguistics does not study real language as spoken by real people.
Instead, it studies an “idealized, homogenous speaker-hearer community.” That is, it assumes
that everyone has exactly the same internal grammar and vocabulary, that everyone is a carbon
copy of everyone else. Brown ably shows through careful empirical studies that this idealization
is not at all justified. She shows significant differences in the way men and women react to
certain words. She gives examples of regional differences in the way certain words are used.
And she shows that very advanced students of English in China are influenced by their culture
in the connotations they give certain words. She illustrates the flexibility of humans in dealing
with language, a flexibility which is not predicted by mainstream linguistics.
My third colleague and Barker lecturer, Jerry Larson, described the state of the art in regard to
technology in language learning. He described many new developments that allow more
sophisticated access to information, from text to sound to pictures to motion video. But he
acknowledged that for a computer to evaluate the appropriateness of the speaking and writing of
a student, when there is not just one predetermined response, we will need software that is “far
more sophisticated than any currently available.” He rightly points out that such software would
have to be able to recognize not just grammar but meaning and take into account the context of
what is said and adjust for cultural factors.
This section of the paper was supposed to explore whether mainstream linguistics adequately
addresses the types of translation difficulty I identified in the first section. I can now answer,
with the support of my colleagues, in the negative. All three types of difficulty required a
sensitivity to meaning, not just a mechanical attention to which words are used and how they are
related grammatically. If mainstream linguistics cannot come to the rescue of those who want to
program a computer to translate more like a person, then what kind of linguistics would it take?
It is clear that it would take some approach to language that deals directly, not peripherally, with
meaning. It is less clear what that approach should be. When you start working with meaning
and try to pin it down so that it can be programmed into a computer, you begin to sympathize
with the reluctance of mainstream linguistics to deal with meaning. And you come up against
some pretty big issues in philosophy. For example, you eventually have to deal with the question
of where meaning comes from. Are meanings already out there somewhere before we even
make up words for them? Or do we create meanings out of nothing? How do we manage to
communicate with others?
Some approaches to meaning assume that there is one basic set of universal concepts on which
all other concepts are based. In this approach, which is sometimes called objectivism and dates
back at least to Descartes, everyone, good or evil, must deal with these same starting concepts. I
begin with the assumption that meanings are not absolutely imposed on us from the nature of the
universe but that they are not entirely arbitrary either. Then where does meaning come from? I
will now discuss a key factor that I believe to be missing from current theories of language. An
approach to language that incorporates this factor should bring us closer to dealing adequately
with meaning. Such an approach should guide us in the design of a computer that could translate
like a person.
A Key Factor That Is Missing from Current Theories
That key factor which is missing from current theories is agency. By agency, I mean the
capacity to make real choices by exercising our will, ethical choices for which we are
responsible. I will show a connection between agency and meaning. And since I have already
shown that to translate we must consider meaning, I will then have shown that there is a
connection between agency and translation. Any 'choice' that is a rigid and unavoidable
consequence of the circumstances is not a real choice that could have gone either way and is
thus not an example of agency. A computer has no real choice in what it will do next. Its next
action is an unavoidable consequence of the machine language it is executing and the values of
data presented to it. I am proposing that any approach to meaning that discounts agency will
amount to no more than the mechanical manipulation of symbols such as words, that is, moving
words around and linking them together in various ways instead of understanding them.
Computers can already manipulate symbols. In fact, that is what they mostly do. But
manipulating symbols does not give them agency and it will not let them handle language like
humans. Symbol manipulation works only within a specific domain, and any attempt to move
beyond a domain through symbol manipulation is doomed, for manipulation of symbols
involves no true surprises, only the strict application of rules. General vocabulary, as we have
seen, involves true surprises that could not have been predicted.
The claim that agency must be included in an approach to meaning is perhaps unexpected. I will
draw on five different sources to support this claim: (1) some work by Terry Warner (BYU
Dept. of Philosophy); (2) some work by John Robertson (BYU Dept. of Linguistics); (3) some
thoughts on physics by Jack Cohen, a biologist, and Ian Stewart (a mathematician); (4) some
work on neural science by Antonio Damasio; and (5) an analysis of Shakespeare’s Othello by
Nancy Christiansen (BYU Dept. of English). So far as I know, these various parties have never
collaborated, yet they are presenting various pieces of what is beginning to look like a coherent
framework of support for the importance of agency in fully explaining human language.
Terry Warner has been working on the notions of agency and self-deception for many years. I
remember studying his writings on the subject already back in 1982. But at the time, I did not
see the connection with translation. Of course, there is a connection between translation and
language. You need to know at least two languages in order to translate. So the key question is
whether language depends on agency. If it does, then translation depends on agency, too, at least
sensitive translation of general language texts. Then, a few years ago, the BYU Dept. of
Philosophy organized a seminar on the philosopher Levinas. This brought Terry and me together
in a new way and eventually resulted my seeing a connection between agency and language and
in the writing of a joint paper, which has now been expanded into a book (Melby and Warner,
1995). I will not talk here about our general collaborative work, but only of the use we have
made of Levinas. Levinas talks about otherness. Someone who is ‘other’ is outside of you and
not under your control. A physical object can, of course, be outside of you yet totally under your
control. A physical object can be under your control intellectually, if in no other way, in that you
can include a representation of it in a system of ideas that enables you to label it exhaustively
and predict its behavior.
We totalize objects in the physical world when we bring them totally under our control. Levinas
points out that when we attempt to totalize other humans, we are treating them like objects
rather than like humans. But we speak and listen only on the presumption that we are
communicating with beings who are not objects but beings with an inner life of their own, just
like ours, whose background and individuality we can take into account and who can take into
account our background and individuality. That kind of language, not as idealistically
represented as if it were a domain language, but in its dynamic reality, has ethics as at least part
of its foundation. Note that we have not said that ethics is based on language; we have said that
language is based on ethics, making ethics logically prior to language. We present this unusual
notion in more detail in chapter 4 of our book and Levinas develops it at length in some of his
writing. In order to make ethical choices we must have agency, that is, we must be agents.
Unless we regard others as agents, just like us, who in turn regard us as agents, then many key
notions that are a basis for general vocabulary become meaningless. Without this interacting
agency, there is no responsibility, no empathy or indifference, no blame, and no gratitude. So
much becomes missing from language that what is left can be described as a technical domain
and handled by a computer. Agency is not a layer on language; dynamic general language is a
layer on agency. Without agency, we are reduced to the status of machines and there is no
dynamic general language. Without dynamic general language, we would translate like
computers and there would be no truly human translation as we now know it. Thus lack of
agency is one factor that keeps computers from translating like people.
As I re-read John Robertson’s Barker lecture, I noticed that on page 15 he points out that if
language were just a machine that tells whether or not a sentence is grammatical, then language
would not allow personal relations with God and other humans. He notes that there was a war in
heaven a long time ago. This war is mentioned in the New Testament (Revelation 12:7).
According to other ancient accounts in the Pearl of Great Price quoted by Robertson, the main
issue of the war in heaven was whether or not people would have agency. Happily, the proagency team won out. Our agency is a prized possession. Neal Maxwell, at the October 1995
General Conference of our university's sponsoring institution, speaking of will, an essential
element of agency as I have defined it, said, “Our will is our only real possession.” The antiagency team, lead by Lucifer, would have totalized all humans and there would have been no
will, no agency, and thus no human language as we know it. We would be like computers
sending meaningless data back and forth.
Robertson is exploring an approach to language which, unlike mainstream linguistics, is
compatible with agency. Robertson has intensely studied the works of C.S. Peirce and finds in
them an approach to language that is compatible with agency. Hopefully, I will be able to
further explore Peirce with Robertson in the future to better see how it applies to translation. But
initially it would appear the Robertson's approach to language is compatible with the Warner
approach in that they both include agency as essential to fully human language.
We now turn from philosophy to physics. Bear with me while I attempt to make a connection
between them. The issue I am concerned with is whether our current understanding of physics is
compatible with agency. As a youth, I had the impression that physics viewed the world as
entirely deterministic. In other words, what will happen next is supposedly determined exactly
and precisely by the current state of the physical universe. In a deterministic view of physics,
there is no room for human agency because we are part of the deterministic system. If there is no
agency, then it should be possible to program a computer to do anything a human can. So it
would be nice if physics allowed for agency.
The view of the brain as a deterministic machine is still held by very intelligent people. For
example, Patricia Churchland, Professor of Philosophy at the University of California, San
Diego, recently (October 12 and 13th, 1995) gave a series of invited lectures on our BYU
campus. Two of her titles are revealing: “Understanding the Brain as a Neural Machine” and
“Am I Responsible If My Brain Causes My Decisions?” From my own attendance at one of her
lectures and from reports of a colleague, it is clear the Churchland holds the view that we have
no real agency since our future decisions are completely determined by the current state of the
machine we call a brain and by input our brain receives from the outside. However, as we will
shortly see, the view of the universe as purely deterministic is out of date in physics.
In their book, The Collapse of Chaos, Cohen and Stewart (1994)take the reader on a tour of
modern reductionist science. In the reductionist approach, as already mentioned in the report on
John Robertson’s Barker lecture, the complexity of the world around us is analyzed in terms of
simpler constituents that are linked together by relatively simple laws—the laws of nature.
Typical examples of successful reductionism are the equations for electromagnetic phenomena
already referred to in the summary of the Robertson Barker lecture and the equations predicting
the motions of the planets using Newton's laws. As Robertson has pointed out, an unwise use of
reductionism has been damaging in linguistics, but I had assumed that it had been uniformly
successful in physics. However, in the past decade or so, some of the implications of chaos
theory have begun to sink in. Now even classical physics is not seen as entirely deterministic,
even if it is exact in analyzing past events and predicting many future events. There are systems
such that even the tiniest differences in initial conditions can lead to large differences at some
future time.
Cohen and Stewart, who challenge the assumption that reductionism is sufficient even in
physics, ask the intriguing question, if complexity is explained by reductionism, then what
explains the simplicity we see around us? As one example, they consider crystals. The structure
of a crystal is not readily explained in terms of the detailed vibrations of individual atoms.
However, the structure of a crystal is probably influenced by the tendency to minimize energy,
and this tendency is contextual rather than reductionistic. They go so far as to state, “We are
surrounded by evidence that complicated systems possess features that can’t be traced back
[solely] to individual components.” (pp. 426-427) In other words, reductionism is insufficient to
explain the physical universe. William E. Evenson of the BYU Physics Department puts it this
way (personal communication): “You have to make sure that the individual components are selfconsistently adjusted for the context.” You can’t blindly build everything up from individual
components without some notion of the big picture. That is quite an admission for a scientists
and mathematicians. Cohen and Stewart do not claim that we must believe in God. Indeed, the
claim that belief in God is a necessary consequence of science would be incompatible with
agency, since there would be no room for faith.2 But they point out that modern physics is not
incompatible with a belief in God. They even refer to an interpretation of physics that leaves
room for human agency. Cohen and Stewart, along with many others, discount some of the
speculations of Roger Penrose (1989 and 1994), a mathematician who thinks that consciousness
comes from quantum effects in a certain part of the brain. But they agree that the question of
consciousness is an important one.
The interpretation they support comes from Freeman Dyson (Cohen and Stewart, 1994, p. 272)
and does not depend on the details of the brain. In quantum mechanics, it is well known that you
cannot measure the exact position and speed of a subatomic particle without influencing the
position and speed of the particle by the process of measuring. This introduces a truly random
element into the physical world, which means that the future is not absolutely determined by the
past.3 Dyson says that quantum mechanics describes what a system might do in the future, while
classical mechanics describes what the system ended up doing in the past. He suggests that our
consciousness may be at the moving boundary between future and past, that is, the present. This
interpretation of physics says that the future cannot be computed exactly even though the past
can be analyzed exactly, leaving open the possibility of free will and thus agency through
choices in future action. Hopefully, I have now made a convincing connection between physics
and the philosophy of agency.
Dyson's explanation is reminiscent of the way word meanings shift. They are unpredictable in
advance, as in the treacle example, but they can always be analyzed when in the past and a
motivation can be established in retrospect. Cohen, Stewart, and Dyson have opened up to me a
new view of physics. This new view is compatible with both modern physics and with a
linguistics based on agency rather than deterministic generation of sentences.
Until recently, I assumed that the highest levels of translation would require a personal
understanding of emotions, but I did not see any connection between emotions and other mental
functions needed for human-like translation. From brain science comes surprising support for a
connection between emotion and human reasoning. Human reasoning is an essential aspect of
agency. What good does it do to have the ability to make choices if one cannot use even
common sense reasoning in making decisions. Now, on the basis of recent studies, the need for
emotions is not a separate requirement for human-like translation. Agency and human reasoning
ability imply feeling emotions, because without emotions, human reasoning is impaired.
Antonio Damasio, a well-respected neurologist, has published an intriguing book, Descartes'
Error (Damasio 1994), which challenges the claim made by Descartes that reason and emotion
should be kept separate. Damasio draws on case studies of unfortunate people who are the
victims of damage to a certain area of the brain, damage that robs them of the ability to feel
emotions. Damasio shows conclusively that the inability to feel emotions hinders their ability to
reason normally and make common sense decisions. For one thing, they become insensitive to
punishment. In a way they may lose some part of their agency, since they can no longer feel
emotions. Considerably more work is needed, probably in the form of masters and doctoral
theses, before firm conclusions can be drawn. But in these cases of brain damage, along with a
loss of common sense, I predict there will be a detectable loss of ability to produce sensitive
translations of certain kinds of texts well, unless the patient's memory of having felt emotions is
sufficient to maintain a full capability in language. This discussion should be continued as more
evidence accumulates.
Finally, we turn back to Shakespeare and find that he may have understood the connection
between language and agency all along. Nancy Christiansen (1994) points out that Othello is
trapped because he can only see one interpretation of events at a time. We could say that he
loses some of his agency by getting trapped in a domain. Iago, on the other hand, is acutely
aware that multiple interpretations of the same facts are possible, but denies some part of his
agency by denying any connection between ethics and choices. Shakespeare, meanwhile, sits
back and sees all sides. He recognizes agency (which is ethics in action) as the basis of
language. I wonder what would have happened if Shakespeare had been chosen as the linguist of
his day. Perhaps everyone would be convinced that agency is needed for human-like translation.
This effort to balance agency and determinism would then be much ado about nothing.
Shakespeare saw the wave of determinism that has engulfed our generation, and saw beyond it.
Great literature was never taken in. Further dialogue with Christiansen on agency and language
is in order.
These five sources fit together in that they all are compatible with the claim that agency is
essential to the richness of normal human language, as opposed to machine-like domain
language. Warner speaks of both language and agency being based on ethics. Robertson claims
that agency is essential to the development of relationships. Cohen, Stewart, and Dyson show
that agency is compatible with modern physics. Damasio shows that fully human reason, which
is essential to agency, is tied to emotions. And Christiansen shows us that Shakespeare
understood the connection between ethics, language, and agency long before I started thinking
about it.
Our concepts are not based on some absolute self-categorization of the physical universe. They
are based in part on the ethical dimension of our relationships with others. Our agency, which
includes both emotion and reason and the ability to choose how we will respond to demands
placed on us by others, is the basis for human language as opposed to machine-like language.
Finally, we can answer the question of this paper. A computer cannot translate more like a
person because it lacks, among other things, agency. It won't suffice to store massive amounts of
information. Without agency, information is meaningless. So a computer that is to handle
language like a human must first be given agency. But we should be careful, because if we give
agency to a computer it may be hard to get it back and the computer, even if it chooses to learn a
second language, may exercise its agency and refuse to translate for us. Douglas Robinson
(1992) puts it well. He asks whether a machine translation system that can equal the work of a
human might not “wake up some morning feeling more like watching a Charlie Chaplin movie
than translating a weather report or a business letter.”
Endnotes
1. An example of the need to be sensitive to cultural factors is the translation of
descriptions of items on a menu in a restaurant. Last year while in Paris, I passed by a
billboard outside a well-known restaurant. The billboard advertised a dish called steak
tartare. The description in English mentioned that it included fresh ground beef and egg,
but failed to mention that the ground beef is served completely raw. In fact, this dish
typically consists of raw ground beef mixed with spices and topped with a raw egg and
bits of raw onion. For an American tourist passing by, there was not a clue that the meat
was served raw. For a British tourist, this may be common knowledge, and for a
Frenchman, it is no big deal that the meat is raw. Their ‘well done’ is more like our
‘rare,’ and they sometimes order a steak bleu, literally, ‘blue,’ meaning barely warmed
over a flame but not cooked in the American sense of meat preparation. An American
could easily think that the word tartare refers to tartar sauce and order the dish thinking
that it would be strange to serve tartar sauce with beef instead of fish but certainly expect
the meat to be cooked. This example shows the importance of being aware of the
differences between cultures when translating.
2. Douglas Adams, in The Hitchhiker's Guide to the Galaxy, gives us a whimsical account
of why science should not attempt to prove the existence of God. The account is
particularly appropriate for this paper since it involves translation.
In Adams’ novel (pp. 58-60), there is a small, yellow fish called the Babel fish that feeds
on brainwave energy. If you place a Babel fish in your ear, you can understand anything
said to you in any language. The Babel fish is thus extraordinarily useful, especially for
someone hitchhiking across the galaxy. However, the story continues, some thinkers
have used the Babel fish as a proof of the non-existence of God. The argument goes like
this. It would be such a bizarrely improbable coincidence that anything so useful as a
Babel fish could have evolved by chance, that we can conclude it did not evolve by
chance. God refuses to allow a proof of his existence, since that would deny faith. But
since the Babel fish could not evolved by chance, it must have been created by God. But
God would not allow a proof of his existence. Therefore, there could be no God.
The silliness of the above argument is intended, I believe, to show the futility of trying to
prove the existence of God, through physics or any other route. Belief in God is a
starting point, not a conclusion. If it were a conclusion, then that conclusion would have
to be based on something else that is firmer than our belief in God. If that something else
forces everyone to believe in God, then faith is denied. If that something else does not
force us to believe in God, then it may not be a sufficiently solid foundation for our
belief.
Adams may also be saying something about translation and the nature of language. I can
speculate on what Adams had in mind to say about translation when he dreamed up the
Babel fish. My own bias would have him saying indirectly that there could be no such
fish since there is no universal set of thought patterns underlying all languages. Even
with direct brain to brain communication, we would still need shared concepts in order to
communicate. Words do not really fail us. If two people share a concept, they can
eventually agree on a word to express it. Ineffable experiences are those that are not
shared by others.
3. There is a famous thought experiment in quantum mechanics devised by Erwin
Schrödinger. A cat is placed in an imprenetrable box, along with a radioactive atom and
a device that detects whether or not the atom has decayed, releasing poison gas when it
does decay. According to one intepretation of quantum mechanics, the cat is in a state of
superimposed life and death until some measurement is made. Many people have written
about this thought experiment, which seems so counterintuitive. Cohen and Stewart do
not challenge the evidence that quantum effects introduce true randomness, but they do
challenge the assumption that the cat can be both alive and dead at the same time. They
discuss what it means to make a measurement, and they suggest that the cat itself knows
what is happening, invoking T. S. Eliot’s poem “The Naming of Cats” (found, among
other places, in The Norton Book of Light Verse, edited by Russell Baker, (c) 1986, W.
W. Norton & Company: New York).
Annex: A showcase example of machine translation
The following English sentence was taken from a source text chosen by a major machine
translation vendor. The source text was translated by computer into French, German, and
Spanish and the output was offered as an example of what machine translation is like when
things go well. Even here, note the different ways the abbreviation ATP was handled. In this
English text, it obviously stands for Advanced Technology Program. However, in the French
text, it was expanded into a French chemical term for the organic chemical that is called
'adenosine triphosphate' in English. This compound, which is broken down from more complex
substances such as sugars, is the immediate source of energy to the cells of our body. Clearly,
this is a serious translation error. The compound adenosine triphosphate, abbreviated ATP, has
nothing to do with the Advanced Technology Program, also abbreviated ATP, except that it has
the same abbreviation. The computer mechanically substituted the full French form for the
chemical use of ATP, demonstrating a lack of understanding of what is being translated. In the
German translation, something strange is going on as well. The abbreviation has been reduced to
lower case, except for the first letter. This is probably because it has been treated as a normal
German noun, and all German nouns are capitalized. In the Spanish translation, the acronym
was left untouched. This is a mistake as well, since the full form was translated and the Spanish
version should be abbreviated as PTA.
* English source text: The purpose of the Advanced Technology Program (ATP) is to assist
United States businesses to carry out research and development on pre-competetive generic
technologies.
* French machine translation: Le but du programme de technologie de pointe (triphosphate
d'adénosine) est aider des entreprises des Etats-Unis d'effectuer la recherche et le
développement sur des technologies génériques précompétitives.
* German machine translation: Der Zweck des Programms der neuen Technologie (Atp) ist,
Staatgeschäfte zu unterstützen, Forschung und Entwicklung auf vorwettbewerblichen
generischen Technologien durchzuführen.
* Spanish machine translation: el propósito del programa de la tecnología avanzada (ATP) es
asistir a los negocios de Estados Unidos realizar la investigación y el desarrollo en tecnologías
genéricas precompetitivas.
References
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•
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Cohen, Jack and Ian Stewart. (1994) The collapse of chaos: discovering simplicity in a
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